scholarly journals Synthetic Data for Small Area Estimation in the American Community Survey

2013 ◽  
Author(s):  
Joseph W. Sakshaug ◽  
Trivellore Raghunathan
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Synthetic Data ◽  
Community Survey ◽  
American Community Survey ◽  
Area Estimation

scholarly journals Spatiality in small area estimation: A new structure with a simulation study

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Yadollah Mehrabi ◽  
Amir Kavousi ◽  
Ahmad-Reza Baghestani ◽  
Mojtaba Soltani-Kermanshahi
Keyword(s):  
Spatial Correlation ◽  
Small Area ◽  
Small Area Estimation ◽  
Synthetic Data ◽  
Practical Applications ◽  
Small Areas ◽  
Area Estimation ◽  
Novel Structure ◽  
Specific Behaviour ◽  
Average Mean Square Error

In numerous practical applications, data from neighbouring small areas present spatial correlation. More recently, an extension of the Fay–Herriot model through the Simultaneously Auto- Rregressive (SAR) process has been considered. The Conditional Auto-Regressive (CAR) structure is also a popular choice. The reasons of using these structures are theoretical properties, computational advantages and relative ease of interpretation. However, the assumption of the non-singularity of matrix (Im-ρW) is a problem. We introduce here a novel structure of the covariance matrix when approaching spatiality in small area estimation (SAE) comparing that with the commonly used SAR process. As an example, we present synthetic data on grape production with spatial correlation for 274 municipalities in the region of Tuscany as base data simulating data at each area and comparing the results. The SAR process had the smallest Root Average Mean Square Error (RAMSE) for all conditions. The RAMSE also generally decreased with increasing sample size. In addition, the RAMSE valuess did not show a specific behaviour but only spatially correlation coefficient changes led to a stronger decrease of RAMSE values than the SAR model when our new structure was applied. The new approach presented here is more flexible than the SAR process without severe increasing RAMSE values.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

scholarly journals [Small Area Estimation: An Appraisal]: Rejoinder

1994 ◽  
Vol 9 (1) ◽  
pp. 90-93 ◽  
Author(s):  
M. Ghosh ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation
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